R Notebook

library(tidyverse)
library(sf)
library(maps)
library(plotly)

italy <- map_data("italy")

catania <- italy %>% 
  filter(region == "Catania")

catania_crime <- read_csv("Sicily2026_Catania_IncomeCrime_ONE.csv")

# Plot the base map
italy_map <- ggplot() +
  geom_polygon(data = italy, aes(x = long, y = lat, group = group), fill = "white", color = "lightgrey") + 
    geom_polygon(data = italy, aes(x = long, y = lat, group = region), fill = NA, color = NA) + 

  geom_polygon(data = catania, aes(x = long, y = lat, group = region), fill = "red", color = "red") +
  #geom_point(data = catania_crime, aes(x = Longitude, y = Latitude, group = CrimeTier, color = CrimeTier)) +
  coord_map() +
  theme_void()# +
 # labs(title = "Italy Base Map")

ggplotly(italy_map)

catania_crime <- catania_crime %>% 
  filter(IncomeTier != "Baseline") %>% 
  mutate(visitability = factor(StyleKey, levels = c(
                                                    "Income=Higher | Crime=Lower", 
                                                    "Income=Mid | Crime=Lower",
                                                    "Income=Mid | Crime=Mid",
                                                    "Income=Lower | Crime=Mid",
                                                    "Income=Lower | Crime=Higher"),
                              labels = c(1,2,3,4,5)
                                                   ),
visitability = factor(visitability, labels = c(
                                                    "Income=Higher | Crime=Lower", 
                                                    "Income=Mid | Crime=Lower",
                                                    "Income=Mid | Crime=Mid",
                                                    "Income=Lower | Crime=Mid",
                                                    "Income=Lower | Crime=Higher"),
                              levels = c(1,2,3,4,5)
                                                   )
)

# Plot Catania are only
catania_map <- ggplot() +
  
  geom_polygon(data = catania, aes(x = long, y = lat, group = group), fill = "white", color = "lightgrey") +
  #geom_point(data = catania_crime, aes(x = Longitude, y = Latitude, group = CrimeTier, color = CrimeTier)) +
  #geom_point(data = catania_crime, aes(x = Longitude, y = Latitude, group = IncomeTier, color = IncomeTier)) +
  geom_point(data = catania_crime, aes(x = Longitude, y = Latitude, group = Name, color = visitability)) +
  coord_map() +
  theme_void() +
 labs(title = "Catania Region") +
 scale_color_manual(values = colorRampPalette(c("darkgreen", "red"))(5)) 

ggplotly(catania_map)

ai_shape <- st_read("C:/Users/ndyet/Git/Misc/Italy_maps/Catania_Quartieri_IncomeCrime_POLY/Catania_Quartieri_IncomeCrime_POLY.shp")

Reading layer `Catania_Quartieri_IncomeCrime_POLY' from data source 
  `C:\Users\ndyet\Git\Misc\Italy_maps\Catania_Quartieri_IncomeCrime_POLY\Catania_Quartieri_IncomeCrime_POLY.shp' using driver `ESRI Shapefile'
Simple feature collection with 35 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 15.0305 ymin: 37.4485 xmax: 15.1225 ymax: 37.5595
Geodetic CRS:  WGS 84

ai_map <- ggplot() +
  geom_polygon(data = catania, aes(x = long, y = lat, group = group), fill = "white", color = "lightgrey") +
  geom_sf(data = ai_shape, aes(fill = crime_tier, group = municipio), color = "grey") + # Counties
 # geom_sf(data = sf_zip_reduce_pol, fill = 'lightblue', color = 'lightblue') +#Surveyed zip codes
 # geom_sf(data = states_pol, fill = NA, color = "black", linewidth = 1) + # State Lines
#  labs(title = "2022 RBSA Encachment Area", 
 #      caption = "Source: 2020 US Census & 2022 RBSA survey") +
  theme_void() 

ggplotly(ai_map)

ai_shape2 <- st_read("C:/Users/ndyet/Git/Misc/Italy_maps/Sicily2026_POLYGONS/Sicily2026_POLYGONS.shp")

Reading layer `Sicily2026_POLYGONS' from data source `C:\Users\ndyet\Git\Misc\Italy_maps\Sicily2026_POLYGONS\Sicily2026_POLYGONS.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 15.0305 ymin: 37.408 xmax: 15.2945 ymax: 37.8565
Geodetic CRS:  WGS 84

ai_map2 <- ggplot() +
  geom_polygon(data = catania, aes(x = long, y = lat, group = group), fill = "white", color = "lightgrey") +
 # geom_sf(data = ai_shape2, aes(fill = crime_idx, group = municipio), color = "grey") + # Counties
  geom_sf(data = ai_shape2, aes(fill = as.factor(night_idx), group = crime_idx), color = "grey") + # Counties

 # geom_sf(data = sf_zip_reduce_pol, fill = 'lightblue', color = 'lightblue') +#Surveyed zip codes
 # geom_sf(data = states_pol, fill = NA, color = "black", linewidth = 1) + # State Lines
#  labs(title = "2022 RBSA Encachment Area", 
 #      caption = "Source: 2020 US Census & 2022 RBSA survey") +
  theme_void() 

ggplotly(ai_map2)